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Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization

Summary: Progressive Shading scales package-query ILP optimization to billions of tuples via hierarchical refinement, avoiding SketchRefine’s premature pruning under tight constraints. Dynamic Low Variance partitioning and custom Dual Reducer/Parallel Dual Simplex solvers deliver adaptive, orders-of-magnitude faster processing. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13551
Venue
VLDB
Year
2024
Pagerank
5.5177726e-05
Overall Rank
7,964 | 45.37%
DOI
10.14778/3641204.3641222

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{mai_vldb24,
        title = {{Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization}},
        author = {Mai, Anh L. and Wang, Pengyu and Abouzied, Azza and Brucato, Matteo and Haas, Peter J. and Meliou, Alexandra},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {5},
        pages = {1146--1158},
        doi = {10.14778/3641204.3641222},
        url = {https://doi.org/10.14778/3641204.3641222},
        year = {2024}
}

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Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,823 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.0893814e-05
3,144 Interactive Data Exploration Using Semantic Windows 2014 SIGMOD 7.7134729e-05
3,214 Searchlight: Enabling Integrated Search and Exploration over Large Multidimensional Data 2015 VLDB 7.6325181e-05
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